Application Potentials of Chinese Knowledge Platforms |
Digital platforms for knowledge transfer in research and education
| Journal | Industry 4.0 Science |
| Issue | Volume 42, 2026, Edition 3, Pages 84-93 |
| Bibliography | Share | Cite | Download |
Abstract
Keywords
Article
In developed industrial societies, knowledge represents a strategic resource. It is a driving force for innovation and sustainable business development. Competitive advantages for businesses increasingly depend on the ability to mobilize and apply knowledge [1]. Digital knowledge platforms are therefore intended to promote knowledge transfer and knowledge mobilization [2]. Fundamental terms and concepts of platform-based knowledge transfer are discussed below.
Understanding and definitions
Data alone constitutes isolated, meaningless facts [3]. Information arises when data is organized and interpreted. Action-guiding knowledge, which is based on information, is shaped by personal experiences and arises through engagement with a subject matter [4]. Knowledge is viewed as the interplay of implicit (i.e., experience-based, routine) and explicit (i.e., articulable, documentable) knowledge.
The importance of knowledge sharing and transfer
Knowledge is not merely the resource of an individual; it is closely embedded in a social and organizational context. That is to say, knowledge becomes effective when shared. Knowledge transfer describes the transmission of knowledge to recipients for individual contextualization [5]. This requires high-quality information resources [6].
Knowledge sharing and transfer are based on socialization and externalization. Socialization refers to the transmission of tacit knowledge through shared experiences, interactions, and informal exchange. Externalization means the conversion of implicit knowledge into explicit knowledge through codification [7]. Through externalization, knowledge becomes comprehensible and usable for others. In this way, personal experience can become collectively available knowledge.
During codification, differing patterns of perception and interpretation between participants must be accounted for [8]. The transfer process is divided into phases: acquisition, communication, application, acceptance, and assimilation [9]. Learning experiences during the assimilation phase provide further impetus to enhance the transfer process.
Objective and methodology of the study
The scientific literature identifies various design criteria for enhancing the quality of knowledge transfer. The central question of this study is: To what extent does the implementation of design criteria in digital knowledge platforms influence the perceived functionality and quality of knowledge transfer? This question is empirically examined through an analysis of leading Chinese knowledge platforms. The study combines theory and empirical research by using theoretical concepts of knowledge transfer to identify success-relevant design criteria for knowledge platforms and empirical findings to clarify and further develop these.
A literature review led to the identification of seven condensed success criteria for knowledge transfer. Building on this, nine Chinese knowledge platforms were identified and categorized according to their transfer logic and functional scope. In an online survey, 153 usersassessed the platforms’ fulfillment of these criteria. The ensuing discussion of the survey results highlights application potential and areas for improvement in platform-based knowledge transfer. Finally, the Chinese system is placed within an international context.
Identification of design criteria for knowledge transfer
Design criteria for knowledge transfer were identified through a systematic literature review in the databases “Science Direct,” “Springer Professional,” and “Google Scholar.” The review primarily drew on English-language literature; the search terms used were “knowledge transfer” and “university-industry relations.”
In a subsequent step, articles with fewer than 50 citations were excluded. Following a content review, studies that were not related to the identification of design criteria were also eliminated. Ultimately, 26 scientific articles were selected that highlight various design factors in knowledge transfer.
To ensure an objective basis for comparison, the design factors most frequently mentioned in the selected studies were prioritized, while subjective variables—such as absorptive capacity, motivation, or participation—were excluded [10]. This resulted in seven condensed design criteria for knowledge transfer (Fig. 1).
![Figure 1: Condensed design factors for knowledge transfer [10].](https://industry-science.com/wp-content/uploads/2026/06/Braun_Figure1NEW-e1781091183854-1024x539.jpg)
Identification of Chinese knowledge platforms
With the development of the knowledge economy, digital platforms have come to play a central role in the People’s Republic of China’s knowledge and innovation system, which is based on the triple helix model [16].
![Figure 2: Triple helix model in knowledge transfer [16].](https://industry-science.com/wp-content/uploads/2026/06/Fig2_Braun_final-1024x581.png)
These knowledge platforms serve as hubs in a “whole-of-nation” approach that aligns research, education, industry, and policy with national priorities.
They thereby facilitate the interplay between academia, government agencies, and commercial enterprises (Fig. 2). Chinese knowledge platforms have more than 370 million users [17].
Nine leading knowledge platforms were selected and categorized (Fig. 3):
- Platforms by universities and research institutes (selection criterion: academic strength),
- government-organized regional platforms (selection criterion: scientific innovation capacity),
- privately operated platforms (selection criterion: market share).
Knowledge platforms by universities and research institutes
University platforms primarily share explicit information such as patents, research articles, technical reports, or conference papers. Despite similar structures, they differ in their strategic orientation.
Peking University’s Technology Transfer Center follows a research-oriented “0-to-1” transfer model. This model is characterized by long-term development cycles, high investment risks, and above-average returns on investment. An interdisciplinary transfer team acts as a bridge between research and practice, while tiered access controls protect strategic information assets from misuse [17].
Tsinghua University’s technology transfer platform, on the other hand, is based on the application-centered “1-to-100” transfer model. It focuses on the development and testing of laboratory technologies to transform them into marketable and scalable industrial products. Knowledge transfer, here, relies primarily on implicit, technical information assets [18].
The technology transfer platform of the Chinese Academy of Sciences (CAS) follows a “full-chain” innovation paradigm driven by national strategy, which aims for comprehensive government support throughout the entire innovation cycle of so-called “bottleneck” technologies. Over 70% of the innovation projects addressed there are based on national research programs [19].
State-organized regional knowledge platforms
The state-organized regional knowledge platforms follow three specific transfer models (supply-oriented, international-cooperative, and demand-oriented) and combine online with in-person formats (such as discussion forums, roadshows, or trade fairs). They receive financial support from government agencies.
The Beijing-based Zhongguancun Technology Transfer Center is considered the first high-tech development zone in the People’s Republic of China. With 37 universities and 206 research institutions, the region is characterized by a high concentration of scientific institutions and innovative companies. The center focuses on providing knowledge resources for key industries, including information technology and biomedicine [20].
The Shanghai Center for Technology Transfer has established a knowledge-based transfer model focused on cross-border technology transactions. It supports both the introduction of innovative technologies and organizational concepts from abroad and the international dissemination of Chinese research [21].
The technology transfer platform in Shenzhen follows a demand-driven transfer concept. Its focus is on the rapid translation of scientific findings into marketable products. Shenzhen boasts a dynamic innovation ecosystem with a multitude of startups that stimulate knowledge transfer [22].
Privately operated knowledge platforms
Privately operated platforms were selected based on their market position. Zhihu holds a market share of approximately 70% in the field of community-based knowledge transfer. Dedao, by comparison, holds a market share of approximately 30% in the segment of paid knowledge services. The platform BiliBili connects over 500,000 content creators.
Since 2011, Zhihu has been China’s leading knowledge platform with around 100 million active users per month. The decentralized, user-driven transfer model is characterized by openness, bidirectional communication, and interactivity. It enables users to ask and answer questions and provides information in more than a thousand subject areas [23]. Financial incentives promote the production of high-quality, paid information modules, while automated reviews and certifications guarantee information quality [24].
Founded in 2016, the knowledge platform Dedao offers paid information services customized to career-oriented professionals. Here, experts summarize current literature on topics such as economics, technology, philosophy, and psychology in 10- to 30-minute podcasts. Authors are carefully selected and an update system ensures the continuing relevance of the podcasts. A lack of interactivity and heavy compression do, however, limit the depth of the knowledge conveyed [25].
BiliBili was founded in 2009 as a platform for so-called “bullet-comment” videos. Through user-generated content, thematic diversity, and the involvement of academic authors, it has evolved into a leading knowledge platform for topics surrounding technology, education, film, and gaming. The platform promotes knowledge transfer among young users through, among other things, entertaining livestream formats. The entertainment-oriented focus may limit systematic knowledge transfer, but the increasing monetization of content intends to improve its quality [26].
Figure 4 provides an overview of the main features of the selected Chinese knowledge platforms.
User evaluation of the platforms
To identify potential for improvement, the selected transfer platforms were evaluated based on the previously identified design criteria for knowledge transfer. For this purpose, an online survey was developed, primarily targeting researchers and students. The survey not only considers the functional performance of the platforms but also emphasizes users’ subjective experiences in the context of learning and application. The survey is available in Chinese as well as in a German translation [10]; it comprises a total of 16 items and is divided into four sections:
- Personal data;
- User experience;
- Fulfillment of the seven success criteria;
- Suggestions for improvement.
The items are rated using a five-point Likert scale.
A total of N1= 154 participants took part in the non-representative survey. Following a quality control check, N2= 153 data sets were included in the analysis. At 74%, the proportion of male participants is higher than that of female participants. In terms of age distribution, the participants are concentrated in the 23-27 age group (54%). The 18-22 age group comprises 29% of the participants. 12% of the participants are 28 years of age or older and 5% of the participants had not yet reached the age of 18.
In terms of their professional background, the respondents primarily come from technical disciplines:
- Technology management (35 participants),
- automation technology and mechanical engineering (30 participants each),
- electrical engineering and business administration (28 participants each), and
- software engineering (1 respondent).
The majority of participants have extensive experience using these platforms: 54% of participants have been using knowledge platforms for more than two years; 34% of participants have one to two years of experience; 12% of participants have less than one year of experience. Regarding frequency of use, 76% of participants stated that they use knowledge platforms daily, 20% use them several times a week, while 3% use them a few times a month. Only 1% of participants use knowledge platforms less frequently.
Privately operated knowledge platforms are used by 99% of respondents, followed by university and research platforms, at 63%. At 2%, government-organized regional platforms have a limited reach (multiple responses were allowed).
In terms of reasons for use, scientific research is the primary motivation (94% of respondents), followed by participation in discussions (78% of respondents) and the acquisition of professional skills (72% of respondents).
28% of respondents use knowledge platforms to stay informed about economic trends (Fig. 5).
The criteria-specific evaluation of the survey results was conducted summarily for all selected knowledge platforms. On the five-point rating scale, a high score (5 points) indicates strong agreement and vice versa (Fig. 6).
In the accuracy category, the knowledge platforms under review only partially meet user expectations. The survey results show that 46% of respondents rated the accuracy criterion at just 2 (out of 5) points, while another 36% gave it 3 points. Only 6% of respondents rated the platform’s information as very accurate (5 points).
57% of respondents rated the explicitation of implicit knowledge with 2 points, indicating a significant need for improvement. However, the relevance of the information provided was generally recognized by the users. 64% of the respondents consider the information to be very relevant (5 points) and 21% of the respondents rated the relevance of the information as high (4 points).
Regarding the platforms’ usability, most ratings fall in the high and medium ranges. 36% of participants awarded 4 points or more and 42% rated usability with 3 points.
The platform’s accessibility is rated 5 points by 57% of respondents. Only a very small proportion of respondents (1%) awarded a score of 1 in this category.
Sustainability is the highest-rated factor in this survey. 74% of respondents awarded the maximum score and attested to the knowledge platforms’ strong sustainability performance.
The innovation capacity brought about by the use of platforms is rated relatively poorly. A majority of respondents (63%) believe that platforms have only a limited impact on fostering innovative thinking and generating new knowledge; consequently, ratings are predominantly between 1 and 2 points.
Discussion of the results
The study illustrates the differentiated transfer logics of leading Chinese knowledge platforms. It shows that the successfull design criteria for knowledge transfer are implemented differently across knowledge platforms. In the central dimensions—accuracy, explicitation of knowledge, usability, and innovation capacity—the knowledge platforms, however, fall short of user expectations.
Accuracy encompasses both the semantic integrity and the authenticity of information. Unverified user contributions in open exchange formats and misinformation diminish quality and trust. Fake identities and plagiarism undermine the authority of experts and diminish their trustworthiness. Inadequate data maintenance and outdated content limit the accuracy of the available information.
Regarding usability, the participants point to optimization potential in information processing and response speed. To increase usability, semantic search technologies or intelligent information retrieval systems are preferable to keyword searches.
Systematic quality assurance is essential for platform-based knowledge transfer in order to identify misinformation and address copyright infringements. A hybrid approach combining human expertise and time-efficient AI verification algorithms is recommended [27]. The goal is to achieve the highest possible accuracy while keeping maintenance costs reasonable; the degree of regulation plays a decisive role in this regard.
The non-representative group of participants in the study, consisting predominantly of young academics, limits the study’s validity. The perspectives of senior experts in corporate innovation management are insufficiently incorporated. Furthermore, there is a lack of in-depth experience with state-organized regional knowledge platforms. It remains unclear to what extent the cultural characteristics of the Chinese innovation and education system can be transferredto international contexts.
The development and use of knowledge platforms in the PRC fundamentally differ from European and American approaches due to stronger political control, strategic goal orientation, and selectivity in knowledge transfer. International platforms more frequently emphasize plurality, decentralization, and mutual exchange, whereas the PRC embeds knowledge platforms more strongly within a national innovation strategy. In this way, market access for innovative technologies is promoted and international economic dependencies are reduced.
Quality assurance and functionalities
Knowledge platforms are gaining importance in innovation management and knowledge transfer. An overview of leading Chinese platforms illustrates both target-group- and task-specific approaches—ranging from generalist, interactive formats to specialized, fee-based expert forums, each with their own specific advantages and disadvantages. The user study shows that the effective use of knowledge platforms depends not only on functional features but, above all, on quality assurance measures.
The study was conducted as part of the research and transfer project “Connect & Collect” (CoCo). The CoCo project is funded under the program “The Future of Value Creation: Research on Production, Services, and Work” by the Federal Ministry of Research, Technology, and Space (BMFTR) (Ref. 02L 19C000 ff.) and is managed by the Project Management Agency Karlsruhe (PTKA). The authors are responsible for the content of the study.
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